OptiChat: Bridging Optimization Models and Practitioners with Large Language Models

Optimization models have been applied to solve a wide variety of decision-making problems. These models are usually developed by optimization experts but are used by practitioners without optimization expertise in various application domains. As a result, practitioners often struggle to interact with and draw useful conclusions from optimization models independently. To fill this gap, we introduce OptiChat, a natural language dialogue system designed to help practitioners interpret model formulation, diagnose infeasibility, analyze sensitivity, retrieve information, evaluate modifications, and provide counterfactual explanations. By augmenting large language models (LLMs) with functional calls and code generation tailored for optimization models, we enable seamless interaction and minimize the risk of hallucinations in OptiChat. We develop a new data set to evaluate OptiChat’s performance in explaining optimization models. Experiments demonstrate that OptiChat effectively delivers autonomous, accurate, and instant responses. These findings highlight the potential of LLMs to bridge the gap between optimization models and practitioners in the real-world decision-making process. History: This paper’s review was handled by special issue editor Ningyuan Chen for the Virtual Special Issue on GenAI etc. for Business Analytics. Funding: This research was supported by Amazon Research Awards (award to C. Li) and the Davidson School of Chemical Engineering, Purdue University (start-up funding). Data Ethics & Reproducibility Note: The code capsule is available at https://github.com/li-group/OptiChat and in the Supplemental Materials to this article (available at https://doi.org/10.1287/ijds.2025.0074.cd ).